ULDA-based heuristic feature selection method for proteomic profile analysis and biomarker discovery

Uncorrelated linear discriminant analysis (ULDA)-based heuristic feature selection (ULDA-HFS) method was proposed for sample classification and feature extraction for SELDI-TOF MS ovarian cancer data. The ULDA-HFS method includes 4 steps: (1) noise reduction and normalization; (2) selection of discriminatory bins with CHI2 method; (3) peak detection and alignment for each selected bins; and (4) selection of several peaks as potential biomarkers by means of ULDA. As a result, 7 m/z locations were selected in this study; they were 245.3, 559.4, 565.6, 704.2, 717.2, 2667 and 4074.4. To evaluate the classification impression, PCA, PLS-DA and ULDA were performed for discriminant analysis and ULDA obtained the perfect separation. Finally, the 7 selected potential biomarkers were evaluated by ULDA, both sensitivity and specificity were 100%. The 7 m/z values obtained may provide clues for ovarian cancer biomarker discovery. Once the proteins were identified at these m/z locations, it can be used as specific protein for early detection and diagnosis for ovarian cancer.     

Uncorrelated linear discriminant analysis (ULDA): A powerful tool for exploration of metabolomics data

The theory together with an algorithm for uncorrelated linear discriminant analysis (ULDA) is introduced and applied to explore metabolomics data. ULDA is a supervised method for feature extraction (FE), discriminant analysis (DA) and biomarker screening based on the Fisher criterion function. While principal component analysis (PCA) searches for directions of maximum variance in the data, ULDA seeks linearly combined variables called uncorrelated discriminant vectors (UDVs). The UDVs maximize the separation among different classes in terms of the Fisher criterion. The performance of ULDA is evaluated and compared with PCA, partial least squares discriminant analysis (PLS-DA) and target projection discriminant analysis (TP-DA) for two datasets, one simulated and one real from a metabolomic study. ULDA showed better discriminatory ability than PCA, PLS-DA and TP-DA. The shortcomings of PCA, PLS-DA and TP-DA are attributed to interference from linear correlations in data. PLS-DA and TP-DA performed successfully for the simulated data, but PLS-DA was slightly inferior to ULDA for the real data. ULDA successfully extracted optimal features for discriminant analysis and revealed potential biomarkers. Furthermore, by means of cross-validation, the classification model obtained by ULDA showed better predictive ability than PCA, PLS-DA and TP-DA. In conclusion, ULDA is a powerful tool for revealing discriminatory information in metabolomics data.     

Quantitative and wide-ranging profiling of phospholipids in human plasma by two-dimensional liquid chromatography/mass spectrometry

Normal-phase or reverse-phase liquid chromatography has been used in phospholipidomics for lipid separation prior to mass spectrometry analysis. However, separation using a single separation mode is often inadequate, as high-abundance phospholipids can mask large numbers of low-abundance lipids of interest. In order to detect and quantify low-abundance phospholipids, we present a novel two-dimensional (2D) approach for sensitive and quantitative global analysis of phospholipids. The methodology monitors individual glycerolipids and phospholipids through the use of a new quantitative normal-phase, solid-phase extraction procedure, followed by molecular characterization and relative quantification using an ion-trap Orbitrap equipped with a reverse-phase liquid chromatograph, with data processing by MS++ software. The CV (%) of the peak area of each lipid standard was less than 15% with this extraction method. When the method was applied to a liver sample, we could detect more phosphatidylserine (PS) compared to the previous method. Finally, our developed method was applied to Alzheimer's disease (AD) plasma samples. Several hundred peaks were detected from a 60 μL plasma sample. A partial-least-squares discriminant analysis (PLS-DA) plot using peak area ratio gave a unique group of PLS scores which could distinguish plasma samples of Alzheimer's disease (AD) patients from those of age-matched healthy controls.     

Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis

Liquid chromatography/mass spectrometry (LC/MS) followed by multivariate statistical analysis has been successfully applied to the plasma phospholipids metabolic profiling in type 2 diabetes mellitus (DM-2). Principal components analysis and partial least-squares discriminant analysis (PLS-DA) models were tested and compared in class separation between the DM2 and control. The application of an orthogonal signal correction filtered model highly improved the class distinction and predictive power of PLS-DA models. Additionally, unit variance scaling was also tested. With this methodology, it was possible not only to differentiate the DM2 from the control but also to discover and identify the potential biomarkers with LC/MS/MS. The proposed method shows that LC/MS combining with multivariate statistical analysis is a complement or an alternative to NMR for metabonomics applications.     

Identifying individual cell types in heterogeneous cultures using secondary ion mass spectrometry imaging with C60 etching and multivariate analysis

Tissue engineering approaches fabricate and subsequently implant cell-seeded and unseeded scaffold biomaterials. Once in the body, these biomaterials are repopulated with somatic cells of various phenotypes whose identification upon explantation can be expensive and time-consuming. We show that imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS) can be used to distinguish mammalian cell types in heterogeneous cultures. Primary rat esophageal epithelial cells (REEC) were cultured with NIH 3T3 mouse fibroblasts on tissue culture polystyrene and freeze-dried before TOF-SIMS imaging. Results show that a short etching sequence with C(60)(+) ions can be used to clean the sample surface and improve the TOF-SIMS image quality. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used to identify peaks whose contributions to the total variance in the multivariate model were due to either the two cell types or the substrate. Using PLS-DA, unknown regions of cellularity that were otherwise unidentifiable by SIMS could be classified. From the loadings in the PLS-DA model, peaks were selected that were indicative of the two cell types and TOF-SIMS images were created and overlaid that showed the ability of this method to distinguish features visually.     

Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies

Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures makes it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported by a standard and rigorous assessment of whether or not the cluster separation is statistically significant. Here quantification and statistical significance of separation of group centroids in PCA and PLS-DA scores plots are considered. The Mahalanobis distance is used to quantify the distance between group centroids, and the two-sample Hotelling's T² test is computed for the data, related to an F-statistic, and then an F-test is applied to determine if the cluster separation is statistically significant. We demonstrate the value of this approach using four datasets containing various degrees of separation, ranging from groups that had no apparent visual cluster separation to groups that had no visual cluster overlap. Widespread adoption of such concrete metrics to quantify and evaluate the statistical significance of PCA and PLS-DA cluster separation would help standardize reporting of metabonomics data.     

Rapid screening of doping agents in human urine by vacuum MALDI-linear ion trap mass spectrometry

Detection of doping agents in urine frequently requires extensive separation prior to chemical analyses. Gas or liquid chromatography coupled to mass spectrometry has produced accurate and sensitive assays, but chromatographic separations require time and, sometimes, chemical derivatization. To avoid such tedious and lengthy procedures, vacuum matrix-assisted laser desorption ionization (vMALDI) coupled with the linear ion trap mass spectrometry (LIT/MS) technique is tested for its applicability as a rapid screening technique. Commonly used doping agents like nandrolone, boldenone, trenbolone, testosterone, and betamethasone were chosen as study compounds. Different MALDI matrixes like alpha-cyano-4-hydroxycinnamic acid (CHCA), dihyroxy benzoic acid (DHB) with and without cetyl trimethyl ammonium bromide (CTAB), a surfactant, and meso-tetrakis(pentafluorophenyl) porphyrin (F20TPP) were tested. Among them, F20TPP (MW 974.57 Da) was selected as the preferred matrix owing to the lack of interfering matrix peaks at the lower mass range (m/z 100-700). Urine samples spiked with study compounds were processed by solid-phase extraction (SPE) and consistently detected through a linear range of 0.1-100 ng/mL. The limit of detection and lower limit of quantification for all five analytes have been determined to be 0.03 and 0.1 ng/mL, respectively, in urine samples. Testosterone-d3 was used as an internal standard, and the quantitative measurements were achieved by the selective reaction monitoring (SRM) mode. The method was validated and showed consistency in the results. Hence, vMALDI-LIT/MS can be used as a rapid screening method to complement the traditional GC/MS and LC/MS techniques for simultaneous identification, confirmation, and quantification of doping agents in urine.     

Proteomic profile analysis and biomarker discovery from mass spectra using independent component analysis combined with uncorrelated linear discriminant analysis

A strategy based on Independent Component Analysis (ICA) and Uncorrelated linear discriminant analysis (ULDA) was proposed for proteomic profile analysis and potential biomarker discovery from proteomic mass spectra of cancer and control samples. The method mainly includes 3 steps: (1) ICA decomposition for the mass spectra; (2) selection of discriminatory independent components (ICs) using nonparametric Mann-Whitney U-test; and (3) selection of special peaks (m/z locations) as potential biomarkers by executing of ULDA on a mass spectra data set which was reconstructed with the m/z locations that collected from the selected discriminatory ICs. A colorectal cancer data set and an ovarian cancer data set were analyzed with the proposed method. As results, 9 and 10 m/z locations were selected as potential biomarkers for the colorectal and ovarian cancer data set respectively. The classification results of ULDA using the selected potential biomarkers yielded better results than fisher discriminant analysis (FDA) and principal component analysis (PCA), and could distinguish the disease samples from healthy controls on the independent test sets with 100% of sensitivities and specificities for the colorectal cancer dataset and 100% of sensitivity and 96.77% of specificity for the ovarian cancer dataset.     

Metabonomics and biomarker discovery: LC-MS metabolic profiling and constant neutral loss scanning combined with multivariate data analysis for mercapturic acid analysis

In the field of metabonomics, 1H NMR and full scan mass spectrometry methods have usually been combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to detect patterns in biofluids that correspond to specific effects, usually a toxic site effect of a compound. Confounders together with great interindividual variation complicate such analysis in humans, and therefore, metabonomic data are almost restricted to animals. In our study, a constant neutral loss (CNL) scan on a linear ion trap demonstrated increased sensitivity and specificity compared to a full scan approach and was performed to detect mercapturic acids (MA), a class of effect markers. The method was applied to human volunteers administered 50 and 500 mg of acetaminophen (AAP), a model compound known to form MAs. Using a new algorithm to prepare the CNL data for chemometrics, discrimination of control and postdose samples could be performed using PCA and PLS-DA. The loadings plots clearly revealed AAP-MA as a marker, even at low-dose levels. Orthogonal signal correction (OSC) was carried out to investigate background information that is not due to exposure. Surprisingly, the OSC data provided a classification of male and female subjects showing the performance of the new approach.     

Screening of biomarkers in rat urine using LC/electrospray ionization-MS and two-way data analysis

Biofluids, like urine, form very complex matrixes containing a large number of potential biomarkers, that is, changes of endogenous metabolites in response to xenobiotic exposure. This paper describes a fast and sensitive method of screening biomarkers in rat urine. Biomarkers for phospholipidosis, induced by an antidepressant drug, were studied. Urine samples from rats exposed to citalopram were analyzed using solid-phase extraction (SPE) and liquid chromatography mass spectrometry (LC/MS) analysis detecting negative ions. A fast iterative method, called Gentle, was used for the automatic curve resolution, and metabolic fingerprints were obtained. After peak alignment principal component analysis (PCA) was performed for pattern recognition, PCA loadings were studied as a means of discovering potential biomarkers. In this study a number of potential biomarkers of phospholipidosis in rats are discussed. They are reported by their retention time and base peak, as their identification is not within the scope of the study. In addition to the fact that it was possible to differentiate control samples from dosed samples, the data were very easy to interpret, and signals from xenobiotic-related substances were easily removed without affecting the endogenous compounds. The proposed method is a complement or an alternative to NMR for metabolomic applications.     

Structural elucidation and quantification of phenolic conjugates present in human urine after tea intake

In dietary polyphenol exposure studies, annotation and identification of urinary metabolites present at low (micromolar) concentrations are major obstacles. To determine the biological activity of specific components, it is necessary to have the correct structures and the quantification of the polyphenol-derived conjugates present in the human body. We present a procedure for identification and quantification of metabolites and conjugates excreted in human urine after single bolus intake of black or green tea. A combination of a solid-phase extraction (SPE) preparation step and two high pressure liquid chromatography (HPLC)-based analytical platforms was used, namely, accurate mass fragmentation (HPLC-FTMS(n)) and mass-guided SPE-trapping of selected compounds for nuclear magnetic resonance spectroscopy (NMR) measurements (HPLC-TOFMS-SPE-NMR). HPLC-FTMS(n) analysis led to the annotation of 138 urinary metabolites, including 48 valerolactone and valeric acid conjugates. By combining the results from MS(n) fragmentation with the one-dimensional (1D)-(1)H NMR spectra of HPLC-TOFMS-SPE-trapped compounds, we elucidated the structures of 36 phenolic conjugates, including the glucuronides of 3',4'-di- and 3',4',5'-trihydroxyphenyl-γ-valerolactone, three urolithin glucuronides, and indole-3-acetic acid glucuronide. We also obtained 26 h-quantitative excretion profiles for specific valerolactone conjugates. The combination of the HPLC-FTMS(n) and HPLC-TOFMS-SPE-NMR platforms results in the efficient identification and quantification of less abundant phenolic conjugates down to nanomoles of trapped amounts of metabolite corresponding to micromolar metabolite concentrations in urine.     

Detection of synthetic testosterone use by novel comprehensive two-dimensional gas chromatography combustion-isotope ratio mass spectrometry

We report the first demonstration of comprehensive two-dimensional gas chromatography combustion-isotope ratio mass spectrometry (GC×GCC-IRMS) for the analysis of urinary steroids to detect illicit synthetic testosterone use, of interest in sport doping. GC coupled to IRMS (GCC-IRMS) is currently used to measure the carbon isotope ratios (CIRs, δ(13)C) of urinary steroids in antidoping efforts; however, extensive cleanup of urine extracts is required prior to analysis to enable baseline separation of target steroids. With its greater separation capabilities, GC×GC has the potential to reduce sample preparation requirements and enable CIR analysis of minimally processed urine extracts. Challenges addressed include online reactors with minimized dimensions to retain narrow peak shapes, baseline separation of peaks in some cases, and reconstruction of isotopic information from sliced steroid chromatographic peaks. Difficulties remaining include long-term robustness of online reactors and urine matrix effects that preclude baseline separation and isotopic analysis of low-concentration and trace components. In this work, steroids were extracted, acetylated, and analyzed using a refined, home-built GC×GCC-IRMS system. 11-Hydroxyandrosterone and 11-ketoetiocolanolone were chosen as endogenous reference compounds because of their satisfactory signal intensity, and their CIR was compared to target compounds androsterone and etiocholanolone. Separately, a GC×GC-quadrupole MS system was used to measure testosterone (T)/epitestosterone (EpiT) concentration ratios. Urinary extracts of urine pooled from professional athletes and urine from one individual that received testosterone gel (T-gel) and one individual that received testosterone injections (T-shots) were analyzed. The average precisions of δ(13)C and Δδ(13)C measurements were SD(δ(13)C) approximately ±1‰ (n = 11). The T-shot sample resulted in a positive for T use with a T/EpiT ratio of >9 and CIR measurements of Δδ(13)C > 5‰, both fulfilling World Anti-Doping Agency criteria. These data show for the first time that synthetic steroid use is detectable by GC×GCC-IRMS without the need for extensive urine cleanup.     

On-line LC-MS analysis of urinary porphyrins

A reversed-phase high-performance liquid chromatography-mass spectrometry (LC-MS) method is described for the separation and simultaneous analysis of porphyrins related to disorders of heme biosynthesis (uro-, heptacarboxylic, hexacarboxylic, pentacarboxylic, and coproporphyrins). The method involves initial porphyrin esterification and extraction from urine. Detection and quantification is performed from the extracts by separation with a Hypersil BDS column and on-line detection by MS through coupling with an atmospheric pressure chemical ionization interface. The porphyrin esters are detected as protonated molecules [M + H]+. Their mass spectra also exhibit an [M + Na]+ fragment of lower intensity. The analytical performance of this method is compared with those of LC with UV and fluorescence detection. LC-MS used in selective [M + H]+ ion monitoring provides the lowest detection and quantitation limits. In scan mode, this LC-MS method affords, without further isolation or concentration steps, the measurement of mass spectra of unknown compounds present in the urine of patients with altered porphyrin excretion.     

Metabolomic approach to evaluate the toxicological effects of nonylphenol with rat urine

Metabolomics has focused on toxicological applications to (1) understand the mechanisms of toxicity, (2) identify novel biomarkers of toxicity, and (3) provide in vivo assessment in animal models through simple and fast methods to date. The toxicological effects of nonylphenol (NP) were evaluated after intraperitoneal injection of rats with 0, 50, and 250 mg kg(-1) day(-1) NP for four consecutive days. In the nontargeted approach, different extraction conditions were introduced to investigate the effects of NP on rats through gas chromatography/mass spectrometry (GC/MS). The GC/MS data obtained were further analyzed with partial least-squares discriminant analysis to compare toxicological effects between control and treated groups. The targeted approach was also used in combination with GC/MS to quantify endocrine hormones and to identify possible biomarkers in rat urine under optimal extraction conditions. In addition, we considered the metabolic trajectory to examine the metabolite profiles and patterns related to steroid metabolism in rats that were treated with NP, considering both treatment amount and time. The data suggest that tetrahydrocorticosterone and 5alpha-tetrahydrocorticosterone are possible urinary biomarkers of NP-induced toxicity. This metabolomic approach is a promising tool to assist with screening in toxicological studies.     

Quantitation of amphetamine,methamphetamine, and their methylenedioxy derivatives in urine by solid-phase microextraction coupled with electrospray ionization-high-field asymmetric waveform ion mobility spectrometry-mass spectrometry

Amphetamine, methamphetamine, and their methylenedioxy derivatives have been identified and measured in a human urine matrix using solid-phase microextraction (SPME) and high-field asymmetric waveform ion mobility spectrometry (FAIMS) in combination with electrospray ionization (ESI) and mass spectrometric detection (MS). Limits of detection in human urine between 200 pg/mL and 7.5 ng/mL have been achieved. The use of a simple extraction method, SPME, combined with the high sensitivity and selectivity of ESI-FAIMS-MS eliminates the need for chromatographic separation and allows for very rapid sample processing.     

Identification and quantification of flavonoids in human urine samples by column-switching liquid chromatography coupled to atmospheric pressure chemical ionization mass spectrometry

A rapid and sensitive high-performance liquid chromatographic mass spectrometric (HPLC-MS) method is described for the determination and quantification of 12 dietary flavonoid glycosides and aglycons in human urine samples. Chromatographic separation of the analytes of interest was achieved by column-switching, using the first column (a Zorbax 300SB C-3 column) for sample cleanup and eluting the heart-cut flavonoid fraction onto the second column (a Zorbax SB C-18 column) for separation and detection by ultraviolet and atmospheric pressure chemical ionization MS using single ion monitoring in negative mode. The fragmentor voltage was optimized with regard to maximum abundance of the molecular ion and qualifier ions of the analytes. Calibration graphs were prepared for urine, and good linearity was achieved over a dynamic range of 2.5-1000 ng/mL. The inter- and intraassay coefficients of variation for the analysis of the 12 different flavonoids in quality control urine samples were 12.3% on average (range 11.0-13.7%, n = 24, reproducibility) and the repeatability of the assay were 5.0% (mean, range 0.1-14.8%, n = 12). A subset of 10 urine samples from a human dietary intervention study with high and low flavonoid content was analyzed, and the results are reported.     

激发-发射矩阵荧光光谱用于鉴别百合掺假的研究

为了能够快速判别百合是否掺假,利用激发-发射矩阵（EEM）荧光技术对纯百合和掺假百合样品进行了荧光光谱分析,并构建了百合及其掺假百合的荧光指纹特征图谱;然后借助主成分分析-线性判别分析（PCALDA）和偏最小二乘-判别分析（PLS-DA）两种化学模式识别方法,对百合中掺假粉末的种类进行了快速鉴别和分类。实验结果表明：两个分类模型均能根据百合样本的EEM荧光光谱数据准确识别掺假百合样本,且正确分类率均高达95%。利用PCA-LDA和PLS-DA成功建立了快速判别百合掺假的新方法,同时完善了百合荧光指纹特征图谱,有望为建立更全面、更准确地评价百合药材的质量标准体系打下基础。     

Hydrophilic interaction liquid chromatography-tandem mass spectrometry determination of estrogen conjugates in human urine

We report a hydrophilic interaction liquid chromatography (HILIC) separation with tandem mass spectrometry (MS) detection method for analysis of seven urinary estrogen conjugates. HILIC separation employing a mobile phase with high organic solvent content resulted in enhanced electrospray ionization efficiency and MS sensitivity compared with reversed-phase (RP) LC-MS methods. Solid-phase extraction (SPE) was used to further improve the limit of detection and to eliminate interferences for the analysis of urine samples. No hydrolysis or derivatization was required in the sample pretreatment. This SPE/HILIC-MS/MS method provided limits of quantification (LOQs at S/N = 10) for the seven conjugates ranging from 2 to 1000 pg/mL with only 1 mL of urine sample, representing an improvement of 1 order of magnitude over the RPLC tandem MS methods previously reported. This method provided a linear dynamic range of 3 orders of magnitude, recovery of 92-109%, intraday accuracy of 84-109%, intraday precision of 1-14%, interday accuracy of 80-111%, and interday precision of 1-22%. We have successfully applied this technique to determine the seven estrogen conjugates in urine samples of a pregnant woman and found unique concentration changes of six estrogen conjugates at different stages of pregnancy while the concentration of estriol-3-glucuronide (E3-3G) remained constant. We further studied the profiles of individual estrogen conjugates in breast cancer patients before and after treatment and found patient-dependent effects of aromatase inhibitor treatment on estrogen phase-II metabolism, which have not been reported previously. This study demonstrates the potential clinical application of the HILIC-MS/MS technique for sensitive monitoring of the changes of urinary estrogen conjugates in a clinical setting.     

Classification of magnetic resonance images from rabbit renal perfusion

The feasibility of using chemometric techniques for the automatic detection of whether a rabbit kidney is pathological or not is studied. Sequential images of the kidney are acquired using Dynamic Contrast-Enhanced Magnetic Resonance Imaging with contrast agent injection. A segmentation approach based upon principal component analysis (PCA) is used to separate out the cortex from the rest of the kidney including the medulla, the renal pelvic, and the background. Two classifiers (Soft Independent Method of Class Analogy, SIMCA; Partial Least Squares Discriminant Analysis, PLS-DA) are tested for various types of data pre-treatment including segmentation, feature extraction, centering, autoscaling, standard normal variate transformation, Savitsky-Golay smoothing, and normalization. It is shown that (i) the renal cortex contains more discriminating information on kidney perfusion changes than the whole kidney, and (ii) the PLS-DA classifiers outperform the SIMCA classifiers. PLS-DA, preceded by an automated PCA-based segmentation of kidney anatomical regions, correctly classified all kidneys and constitutes a classification tool of the renal function that can be useful for the clinical diagnosis of renovascular diseases.     

Effect of magnetic field strength on NMR-based metabonomic human urine data. Comparative study of 250, 400, 500, and 800 MHz

Metabonomic analysis of urine utilizing high-resolution NMR spectroscopy and chemometric techniques has proven valuable in characterizing the biochemical response to an intervention. To assess the effect of magnetic field strength on information contained in NMR-based metabonomic data sets, 1H NMR spectra were acquired on 250-, 400-, 500-, and 800-MHz instruments, respectively, on the same set of human urine samples collected before and after dietary interventions with milk and with meat proteins. Partial least-squares regression discriminant analyses (PLS-DA) were performed in order to elucidate the ability of the 1H spectra acquired at various field strengths to identify possible spectral differences and discriminate between pre- and postintervention samples. The loadings from PLS-DA contained the same spectral regions, implying that the same metabolites were involved in the discrimination independent of magnetic field strength. The investigation revealed a strong increase in prediction performance and thereby spectral information content when increasing the magnetic field strength from 250 to 500 MHz, while from 500 to 800 MHz the increase was less pronounced.